Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks
|Title||Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Lee-Leon A., Yuen C., Herremans D.|
|Conference Name||16th IEEE Asia Pacific Wireless Communications Symposium (APWCS)|
Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods — (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SWAC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. Next, the classification model correlates the images to a corresponding binary representative. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).